Time-of-flight sensor-assisted iris capture system and method

ABSTRACT

A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This application is a continuation of U.S. patent application Ser. No.12/367,005 entitled “TIME-OF-FLIGHT SENSOR-ASSISTED IRIS CAPTURE SYSTEMAND METHOD”, filed Feb. 6, 2009, the complete subject matter of which ishereby incorporated herein by reference, in its entirety.

BACKGROUND

1. Field of the Invention.

The present invention relates to apparatuses and methods for identifyingpersonnel and, more particularly, to apparatuses and methods foridentifying personnel based on visual characteristics of the irises oftheir eyes.

2. Description of the Related Art.

Iris recognition, or “iris capture” is a method of biometric personalidentification that uses pattern recognition algorithms based on imagesof at least one of the irises of an individual's eyes. Iris recognitionuses camera technology to produce images of the details of the iris.These images are converted into digital templates and providemathematical representations of the iris that are used to identifyindividuals.

Many iris capture systems with low levels of intrusiveness have beenproposed to extend the operational range for iris capture in recentyears. For example, a system has been proposed that consists of multiplesynchronized high-speed, high-resolution, and narrow-field-of-view(NFOV) video cameras, in which system focus is fixed at a portal aboutthree meters away from the camera. This is the first system that couldbe used in practice for iris capture over long distances with a highthroughput (e.g., twenty persons per minute). However, the depth offield (DOF) for each user is limited (e.g., five to twelve centimeters).With the limited DOF, it is possible that a same user would need to passthe portal multiple times before a clear iris image is captured.

Other types of systems employ one high resolution camera with either anautofocus or a fixed focus lens for the capture. The depth informationof the subject is usually needed in these systems, which can beestimated implicitly based on the scale or the size of the capturedtwo-dimensional face, or can be computed based on a pair of high speedvideo cameras using stereo vision. Because the acquired depthinformation based on these methods is not very accurate, these systemsare not robust enough for practical use. For example, the featurematching in the stereo cameras is not always accurate under differentlighting conditions. As a result, the robust sub-pixel matching is noteasy to achieve in practice. Furthermore, the accuracy of a stereovision system also depends on the length of the baseline. High accuracywould require a longer baseline, which make it impossible to build acompact iris capture system for many security applications.

Even though cameras with high shutter speed are often used in thesesystems, users still need to remain still for several seconds so thatthe high quality iris image can be captured. This is because defocusblur can easily happen when a moving subject is captured with asignificant amount of system delay because of the slow movements of thepan-tilt unit or because of a long autofocus time. The accurate depthinformation (i.e., distance from the user to the camera) provided bythese systems could be used for iris deblurring and therefore to enhancethe performance of the whole system.

What is neither disclosed nor suggested in the art is an iris capturesystem with an extended operational range, and that does not rely on theperson remaining motionless for a period of a few seconds or more.

SUMMARY

The present invention provides a less intrusive iris capture system thatcombines a commercial off-the-shelf (COTS) high resolution camera withnear infrared (NIR) illumination and a time-of-flight (TOF) depthsensor.

In one embodiment, the present invention comprises a method ofidentifying a living being, including using a time-of-flight sensor todetermine a location of a face of the living being. An image of an irisof the living being is produced dependent upon the location of the faceas determined by the time-of-flight sensor. The produced image isprocessed to determine an identity of the living being.

In another embodiment, the present invention comprises a system foridentifying a living being. The system includes a time-of-flight sensorpositioned to sense a face of the living being within a predeterminedspace. A camera is positioned to capture an image of an iris of theliving being within the predetermined space. A processor is incommunication with each of the time-of-flight sensor and the camera. Theprocessor receives information about a location of the face of theliving being from the time-of-flight sensor, determines whether one ortwo eyes of the living being are within the field of view of the camera,receives the captured image from the camera, estimates defocus blurkernel, applies deblurring for the captured image if it is blurred, anddetermines an identity of the living being based on the captured image.

In yet another embodiment, the present invention comprises a method ofidentifying a living being, including using a time-of-flight sensor toascertain a 3D or 2D shape of a face of the living being. An image of aniris of the living being is captured. An identity of the living being isdetermined based on the ascertained shape of the face of the livingbeing captured from the TOF sensor and based on the captured image ofthe iris of the living being.

An advantage of the present invention is that it provides an extendeddepth of field (DOF), an enlarged capture volume, and an improvedcapturing speed.

Another advantage is that a time-of-flight (TOF) sensor is used in theiris capture system, which provides accurate depth information from thedepth map and allows the system to locate the three-dimensional eyeposition accurately so that the iris/face can be captured accurately.

Yet another advantage is that the accurate depth information provided bya TOF sensor can allow the system to perform fast capture as the focusposition can be quickly calculated for cameras with adjustable focus. Incontrast, autofocus is usually slow in this case.

Still another advantage is that the depth information provided by theTOF sensor can be used to estimate the blur kernel for the irisdeblurring. Defocus blur can easily occur in such systems, which is anissue that can seriously affect the system performance.

A further advantage is that the depth information provided by the TOFsensor for the subject's face may be used to estimate thethree-dimensional geometry of the subject's face, which can enable a lowcost face recognition system including multimodal (i.e.,three-dimensional face shape and iris) integration.

A still further advantage is that the TOF sensor allows a compact andportable design for iris capture for many security applications (e.g.,access control or border control).

BRIEF DESCRIPTION OF THE DRAWINGS

The above mentioned and other features and objects of this invention,and the manner of attaining them, will become more apparent and theinvention itself will be better understood by reference to the followingdescription of an embodiment of the invention taken in conjunction withthe accompanying drawings, wherein:

FIG. 1 is a block diagram of one embodiment of an iris capture systemaccording to one embodiment of the invention;

FIG. 2 is an operational block diagram of the iris capture system ofFIG. 1;

FIG. 3 is an example of a fitted curve for the measured focus positionsof the camera of the system of FIG. 1 as a function of the depth betweenthe camera lens and the object.

FIG. 4 a illustrates examples of plots of the standard deviation of theblur kernel Gaussian distribution as a function of the focus position ofthe camera of the system of FIG. 1 for various distances between thecamera and the iris according to one embodiment of a method of thepresent invention for visually recognizing an iris.

FIG. 4 b is the plot of FIG. 4 a corresponding to a distance of 3.30meters between the camera and the iris.

FIG. 4 c is the plot of FIG. 4 a corresponding to a distance of 2.97meters between the camera and the iris.

FIG. 4 d is the plot of FIG. 4 a corresponding to a distance of 2.56meters between the camera and the iris.

FIG. 4 e is the plot of FIG. 4 a corresponding to a distance of 2.00meters between the camera and the iris.

FIG. 4 f is the plot of FIG. 4 a corresponding to a distance of 1.58meters between the camera and the iris.

FIG. 4 g is the plot of FIG. 4 a corresponding to a distance of 1.43meters between the camera and the iris.

FIG. 4 h is a plot illustrating how a standard deviation defining a blurkernel distribution appropriate for deblurring may be calculatedaccording to one embodiment of a method of the present invention.

FIG. 5 is a plot of the distributions of image gradients of randomnatural images and of global iris images.

Corresponding reference characters indicate corresponding partsthroughout the several views. Although the drawings representembodiments of the present invention, the drawings are not necessarilyto scale and certain features may be exaggerated in order to betterillustrate and explain the present invention. Although theexemplification set out herein illustrates embodiments of the invention,in several forms, the embodiments disclosed below are not intended to beexhaustive or to be construed as limiting the scope of the invention tothe precise forms disclosed.

DETAILED DESCRIPTION

The embodiments hereinafter disclosed are not intended to be exhaustiveor limit the invention to the precise forms disclosed in the followingdescription. Rather the embodiments are chosen and described so thatothers skilled in the art may utilize its teachings.

Turning now to the drawings, and particularly to FIG. 1, there is shownone embodiment of an iris capture system 20 of the present inventionincluding an NFOV NIR camera 22 with adjustable focus, an NIRilluminator 24, and a depth sensor 26 all in electronic communicationwith a central processor 28. System 20 may capture images of, and detectthe positions of, moving subjects such as a human being 30 or a humanbeing 32 when he approaches a doorway at which camera 22, illuminator 24and sensor 26 are mounted, such as-in a direction indicated by arrow 36.Camera 22 may be installed with a mounting height H and tilt angle asuch that a standoff distance 38 for the user is approximately between1.5 meters and 3.5 meters and the captured iris diameter is above 150pixels. In one embodiment, height H is about 250 centimeters. The widthof a capture volume 40 may be on the order of 20 centimeters. In theembodiment illustrated in FIG. 1, a width 42 of capture volume 40 wherethe image and shape of the taller person 30 are captured is about 17centimeters, and a width 44 of capture volume 40 where the image andshape of the shorter person 32 are captured is about 30 centimeters.There are many devices known for measuring depth information, such asstereo cameras, time-of-flight sensors, and structure lights.

In embodiments in which NFOV camera 22 does not have panning and tiltingcapabilities, the human being whose image and shape are being capturedneeds to look at camera 22 while approaching the doorway. The iriscapture may be triggered at different standoff distances for users withdifferent heights.

Depth sensor 26 may be installed at various positions and orientations.TOF sensor 26 may be positioned very close to NFOV camera 22 to allowfor a more compact design. NIR illuminator 24 can be placed at anylocation so long as it illuminates capture volume 40.

System 20 can be applied to other possible settings in which depthsensor 26 is used. For example, camera 22 may be in the form of a highspeed, high performance video camera. Alternatively, camera 22 may havea fixed focus or adjustable focus based on the distance between thecamera and the user. It is also possible for camera 22 to includepan-tilt capabilities in order to further enlarge the capture volume.

An operational block diagram of system 20 illustrated in FIG. 2. Thethree-dimensional information measured by depth sensor 26 may be used invarious ways within system 20. First, face detection and tracking 46 maybe performed on the up-sampled intensity images 48 captured by depthsensor 26. The three-dimensional position of the eyes may then beestimated from an upper portion of the detected face depth maps. Thenext eye location for the moving subject may be predicted accurately inreal time. For example, time rates of change of the three-dimensionalposition of the eyes may be extrapolated to predict future eyelocations. Second, the three-dimensional position may be used todetermine whether eyes are within the field of view and whether thestand-off distance is within the depth of field. If these two conditionsare satisfied, the NFOV camera may be instructed to perform imagecapturing, as at 50. Third, the depth information may be used todynamically control the focus position of the lens of NFOV camera 22.Finally, the depth information can be used to estimate the blur kernel52 for iris deblurring, as at 53. The deblurring may be useful in aniris recognition algorithm 55. More accurate depth information could beused to predict the speed and future positions of the human being sothat the real or desired focus position can be estimated more accuratelyeven when the system delay exists. The real or desired focus positionmay represent the focus position that is ideal for the future estimatedposition of the human being.

Calibration between NFOV camera 22 and depth sensor 26 may be performed,as at 54. In one embodiment, depth sensor 26 could be a TOF sensor. Manyexisting TOF sensors contain systematic depth bias from the demodulationof correlation function and incident lights, and so calibration, orso-called “precalibration”, of the TOF sensor may obtain a better depthmeasurement. In a first step of a novel calibration method of thepresent invention, a large planar board may be positioned at differentdepths and with different orientations. A robust plane fitting may thenbe applied for the planar board at each position. The depth bias may beestimated by computing the difference between measured depth and thefitted plane. After the calibration of TOF sensor 26, the depthuncertainty may be greatly reduced, especially the depth uncertaintybetween 1.3 and 2 meters. In order to transform the depth in thecoordinate system of TOF sensor 26 to that of NFOV camera 22, a fullsystem calibration may be performed. The NFOV camera with a telephotolens may be approximated as an affine camera. A planar checkerboardpattern is captured at different depths. As the correspondences betweenthe two-dimensional points x from NFOV camera 22 and three-dimensionalpoints X from TOF sensor 26 are known, the projection matrix P can becomputed by minimizing the re-projection errors. The intrinsic andextrinsic matrices may be obtained by RQ decomposition of P.

A second method to calibrate between NFOV camera 22 and TOF sensor 26 isa non-parametric method. A small planar pattern may be positioned atdifferent 3D locations within the field of view of TOF sensor 26. The 3Dlocations visible to the NFOV camera 22 are recorded and furtherconstruct a 3D convex hull in the coordinate system of TOF sensor 26.When TOF sensor 26 is mounted close enough to the NFOV camera 22, thedistance between eyes and NFOV camera 22 may be approximated by thedepth information measured by TOF sensor 26. When a living being entersthe field of view of TOF sensor 26 and either eye's location is insidethe pre-computed convex hull, then the eye is also within the field ofview of NFOV camera 22.

Blur kernel estimation step 52 for iris deblurring is optional. As longas the iris deblurring algorithm needs to use the accurate depthinformation, the depth information provided by TOF sensor 26 may besufficient. When depth information is not available in capturingsystems, some statistics of the captured image (e.g., focus scores) maybe used to estimate blur kernel.

Image blur may be modeled as a convolution process:I=L

h+n  (1)where I, L, h, and n represent the blurred image; un-blurred image;point spread function (PSF) or blur kernel; and additive noise,respectively. For defocus blur, the PSF h depends on the circle ofconfusion R. For cameras with adjustable focus, R is a function of twoparameters based on the typical pin-hole camera model. The twoparameters are the distance from the object to the lens d and thedistance between the lens and the image plane s,

$\begin{matrix}{R = {\frac{Ds}{2}{{\frac{1}{f} - \frac{1}{d} - \frac{1}{s}}}}} & (2)\end{matrix}$where D is the radius of the lens, and f is the focal length of thelens. For cameras with fixed focus s, R is determined only by d.

The PSF h for the defocus blur may be modeled as a Gaussian kernel,

$\begin{matrix}{h = {\frac{1}{2{\pi\sigma}_{h}^{2}}{{\mathbb{e}}^{- \frac{x^{2} + y^{2}}{2\sigma_{h}^{2}}}.}}} & (3)\end{matrix}$Because the captured eye region is usually parallel to the image plane,the PSF h may be shift-invariant.

The blur kernel estimation method of the present invention will now bedescribed with the assumption in place that the depth difference ismeasured. When the fixed focus cameras are used, it is relatively simpleto estimate the kernel. The kernel estimation method of the presentinvention may deal with the more general case, i.e., cameras withadjustable focus. As mentioned above, the depth difference may be mainlycaused by the system delay when a subject is moving.

As the lens focus position p_(f) is proportional to the distance betweenthe lens and image plane s, when the circle of confusion R is smallenough, the relationship between the in-focus position of lens p_(f) andd may be derived based on Equation (2),

$\begin{matrix}{p_{f} = {\frac{d}{{k_{1}d} + k_{2}}.}} & (4)\end{matrix}$

After measuring focus positions from in-focus images at differentdepths, k₁ and k₂ can be easily estimated by curve fitting usingEquation (4). FIG. 3 shows an example of a fitted curve for the measuredfocus positions and depths.

As the standard deviation of the blur kernel Gaussian distribution σ_(h)is proportional to R and s is proportional to p_(f), when d is fixed,the relationship between σ_(h) and p_(f) may be derived, based onEquation (2),σ_(h) =|k ₃ p _(f) +k ₄|  (5)

Although the parameters k₁, k₂, k₃ and k₄ are characteristics of thecamera system, they have no obvious physical meaning or representation.The standard deviation σ_(h), which defines the blur kernel Gaussiandistribution, cannot be measured directly. Thus, the following novelalgorithm of the present invention may estimate σ_(h) and then learn k₃and k₄ accordingly.

In a first step of the algorithm, in-focus and defocused checkerboardimages are captured under different depths and different focuspositions. As in-focus and defocused images are known, only σ_(h) isunknown. The standard deviation σ_(h) is estimated by ar gmin_(σh)∥I−L

h∥₂ ². The subscript 2 in the formula denotes a Euclidean Norm or aL2-Norm.

In a next step, k₃ and k₄ are estimated by argmin_(k3,k4)∥k₃p_(f)+k₄−σ_(h)∥₂ ².

FIGS. 4 a-g show examples of the fitting results for p_(f) and σ_(h)based on Equation (5). FIGS. 4 a-g are plots of the focus position ofcamera 22 versus a standard deviation of the blur kernel distributionfor six different distances between camera 22 and the subject iris. Theplot for each of the six distances is V-shaped, with the origin of the“V” being at the in-focus position corresponding to that distance. Theparameter k₃ may represent the slope of a corresponding V-shaped plot inFIGS. 4 a-g; and parameter k₄ may represent the y-intercept of thecorresponding V-shaped plot. V-shaped plot 60 corresponds to a distanceof about 3.30 meters; V-shaped plot 62 corresponds to a distance ofabout 2.97 meters; V-shaped plot 64 corresponds to a distance of about2.56 meters; V-shaped plot 66 corresponds to a distance of about 2.00meters; V-shaped plot 68 corresponds to a distance of about 1.58 meters;and V-shaped plot 70 corresponds to a distance of about 1.43 meters.

Each of the circles in FIGS. 4 a-g represents an empirically-collecteddata point. The data points at the top (standard deviation=20) of FIGS.4 a-g are the images that are severely blurred. It may not be feasibleto recover these kinds of severely blurred images in practice even witha large kernel size. Hence, these severely blurred images are treated asoutliers and are not included in the estimation.

Based on FIGS. 3 and 4 a-g, it can be concluded that the modelsdescribed in Equations (4) and (5) may be used for real camera systemseven though the derivation of Equations (4) and (5) is based on thetraditional pin-hole camera model. A practical use of the plots of FIGS.4 a-g is to estimate the blur kernel when the subject is moving.

When a user enters the field of view of the capturing system, thethree-dimensional position of the user's eyes after the system delay maybe predicted. When the predicted eye position satisfies the triggeringcondition, the predicted in-focus position {tilde over (p)}_(f) iscomputed using Equation (4) and the image is produced at this position.The correct (i.e., actual) depth at the time of image capture (after thesystem delay) is measured, and the correct or ideal in-focus position p_(f) corresponding to the actual depth measurement is computed. Forexample, assuming the correct or ideal in-focus position p _(f) is 15(as shown as the origin of the V-shaped plot in FIG. 4 h) for an actual,measured depth, a new model can be interpolated (i.e., Equation (5) withdifferent values for k₃ and k₄). The new model is illustrated as thedashed V-shaped plot originating at focus position 15 in FIG. 4 h.Assuming the predicted in-focus position {tilde over (p)}_(f) that wasactually used to produce the iris image is 13.5, as indicated by therectangle at 13.5 in FIG. 4 h, the standard deviation σ_(h) that definesthe blur kernel distribution appropriate for use in deblurring is shownto be approximately 8 in FIG. 4 h. The standard deviation σ_(h) may becomputed by taking the predicted focus position of 13.5 that wasactually used to produce the image, and plugging that value of 13.5 intoEquation (5) along with the values of k₃ and k₄ that correspond to theactual depth measurement (i.e., the actual depth measurement thatcorresponds to an ideal focus position of 15).

The above-described calculation of the blur kernel Gaussian distributionmay be used to unblur a captured blurred image as described in detailbelow. Particularly, the process of image deblurring may be formulatedin the Bayesian framework by Bayes' theorem,P(L|σ_(h),I)∝P(I|L,σ_(h))P(L)

where P(I|L,σ_(h)) is the likelihood that L is the clear image given ablur kernel defined by a Gaussian distribution that is, in turn, definedby a standard deviation σ_(h). P(L) represents the prior on theun-blurred image L. A prior probability, or a “prior”, is a marginalprobability, interpreted as what is known about a variable in theabsence of some evidence. The posterior probability is then theconditional probability of the variable taking the evidence intoaccount. The posterior probability may be computed from the prior andthe likelihood function via Bayes' theorem.

Different priors chosen in this framework may lead to differentdeblurring algorithms with different performances. The novel irisdeblurring algorithm of the present invention may be applied in any iriscapture system to handle defocus blur. The prior on the un-blurred imageL may depend upon three prior components that are based on global andlocal iris image statistics:P(L)=P _(g)(L)P _(p)(L)P _(s)(L).

The first prior P_(g)(L) may be computed from an empirically-determinedglobal distribution of the iris image gradients; P_(p)(L) may becomputed based on characteristics of dark pupil region; and P_(s)(L) maybe computed from the pupil saturation region (i.e., the highlight regionof the pupil that is saturated with intensity values of highbrightness). For general image deblurring, the global distribution ofiris image gradients may be approximated by a mixture of Gaussiandistributions, exponential functions, and piece-wise continuousfunctions. Mixture Gaussian distributions are described in “Removingcamera shake from a single photograph”, R. Fergus, B. Singh, A.Hertzmann, S. T. Roweis, and W. T. Freeman, ACM Transactions onGraphics, 2006; exponential functions are described in “Image and depthfrom a conventional camera with a coded aperture”, A. Levin, R. Fergus,F. Durand, and W. T. Freeman, ACM Transactions on Graphics, 2007; andpiece-wise continuous functions are described in “High-quality motiondeblurring from a single image”, Q. Shan, J. Jia, and A. Agarwala, InSIGGRAPH, 2008, each of which is incorporated by reference herein in itsentirety.

Because the application domain is iris images rather than naturalimages, according to one embodiment of the present invention, the globaldistribution may be computed from iris images only. As illustrated inFIG. 5, the distribution of general natural images (i.e., any imagesfound in nature, such as sky, water, landscape) has a greateruncertainty than the distribution of global iris images. The presentinvention takes advantage of the tight range of the global iris imagestatistics.

As a result of the tighter iris image statistics, the distribution ofiris image gradients is a stronger prior. A two-piecewise quadraticfunction (i.e., a piecewise quadratic function having two separate,continuous portions) may be used to approximate the distribution so thatthe optimization based on this Bayesian problem becomes simpler and moreefficient. A general form of the two-piecewise quadratic function maybe:

${P_{g}(L)} \propto \left\{ \begin{matrix}{{\prod_{i}{\mathbb{e}}^{{a_{1}{({\partial L_{i}})}}^{2} + b_{1}}},{{{\partial L_{i}}} \leq k}} \\{{\prod_{i}{\mathbb{e}}^{{a_{2}{({\partial L_{i}})}}^{2} + b_{2}}},{{{\partial L_{i}}} > k}}\end{matrix} \right.$where ∂L, is the gradient for a pixel and k is the threshold between twofunctions. Such a two-piecewise quadratic function may be represented bythe fitted curve in FIG. 5, wherein the threshold k is at thetransitions between the low frequency and high frequency regions.

The second P_(p)(L) and third P_(s)(L) priors may be computed from thelocal pupil region because the dark pupil region is likely to be smoothas compared with the nearby iris patterns, and the highlight region islikely saturated. Therefore, these two priors may be particularly usefulin recovering nearby iris patterns. As the smooth pupil region tends tohave small gradients that are not sensitive to the defocus blur, and thesaturated highlight region tends to contain the highest intensity, thetwo priors may be computed as following:

${P_{p}(L)} \propto {\prod\limits_{i \in \Omega_{1}}{N\left( {\left. {{\partial L_{i}} - {\partial I_{i}}} \middle| 0 \right.,\sigma_{p}} \right)}}$${{P_{s}(L)} \propto {\prod\limits_{i \in \Omega_{2}}{N\left( {\left. {L_{i} - 255} \middle| 0 \right.,\sigma_{s}} \right)}}},$where Ω₁ is the dark pupil region (i.e., excluding the highlightregion), and Ω₂, is the saturated highlight region within the pupil. Thedark pupil region and the saturated highlight region within the pupilcan be detected by image processing techniques, such as thresholding,erosion and dilation. The 255 term in the P_(s)(L) formula representsthe highest (i.e., whitest) color value on a scale of 0 to 255.

Putting all of these priors together, this iris deblurring problem maybe solved by minimizing an energy function E in the following quadraticform:E∝∥I−L

h∥^(2 +λ) ₁(∥a₁(∂L)²+b₁∥·M₁+∥a₂(∂L)²+b₂∥·M₂)+λ₂(∥∂L−∂I∥²·M₃+∥L−255∥²·M₄),where M₁, M₂, M₃, and M₄, are masks of low-frequency region,high-frequency region, dark pupil region, and highlight region in thepupil; I is the known blurred image captured by the camera lens; h isthe blur kernel, which may be estimated as discussed in detail above;and L is the clear image that is being determined. Thus, given knownvalues for the blurred image I and the blur kernel h, an image L may bedetermined that minimizes E, and this image L may be used as arepresentation of a clear, unblurred version of the produced blurredimage I.

The deblur kernel h can be estimated based on the depth information orfocus scores. If the blur kernel is not known, it is possible to add aGaussian prior in place of the blur kernel in order to convert thenon-blind deconvolution into a blind one, which still can be solved bythe optimization framework.

While this invention has been described as having an exemplary design,the present invention may be further modified within the spirit andscope of this disclosure. This application is therefore intended tocover any variations, uses, or adaptations of the invention using itsgeneral principles. Further, this application is intended to cover suchdepartures from the present disclosure as come within known or customarypractice in the art to which this invention pertains.

1. A method of identifying a living being, comprising the steps of: using a time-of-flight sensor to determine a 3D location of a face or eye of the living being; producing an image of an iris of the living being, the producing being dependent upon the location of the face as determined by the time-of-flight sensor, the producing step including the substeps of: using a camera to capture a visual representation of the iris of the living being; and modifying the captured visual representation based on a focus position of the camera while capturing the visual representation and based on the location of the face of the living being during the capturing, the modifying including estimating a blur kernel based on the focus position of the camera while capturing the visual representation and based on the location of the face of the living being during the capturing; calculating a focus position based on the location of the face of the living being, the estimating of the blur kernel being based in part on the optimal focus position and/or the calculated focus position; and processing the produced image to determine an identity of the living being.
 2. A system for identifying a living being, comprising a time-of-flight sensor positioned to sense a face of the living being within a predetermined space; a camera configured to capture a visual representation of the iris of the living being within the predetermined space; and a processor in communication with each of the time-of-flight sensor and the camera, the processor being configured to: receive information about a location of the face of the living being from the time-of-flight sensor; determine whether one eye or two eyes of the living being are within the field of view of the camera; receive the captured visual representation from the camera; estimate a defocus blur kernel; determine whether the captured visual representation needs to be modified; modify the captured visual representation based on a focal length of the camera while capturing the visual representation and based on the location of the face of the living being during the capturing; and determine an identity of the living being based on the captured or modified image, wherein the camera is configured to determine whether the captured visual representation needs to be modified based on the estimated blur kernel, the processor being configured to estimate a blur kernel based on the focus position of the camera while capturing the visual representation and based on the location of the face of the living being during the capturing.
 3. The system of claim 2 wherein the processor is configured to calculate a focus position based on the location of the face of the living being, the estimating of the blur kernel being based in part on the optimal focus position and/or the calculated focus position.
 4. A method of identifying a living being, comprising the steps of: using a time-of-flight sensor to ascertain a shape of a face of the living being; capturing an image of an iris of the living being, wherein the capturing step includes the substeps of: using a camera to obtain a visual representation of the iris of the living being; determining whether the captured iris image needs to be modified based on an estimated blur kernel; and modifying the visual representation based on a focus position of the camera while obtaining the visual representation and based on a location of the face of the living being, as sensed by the time-of-flight sensor, during the obtaining, wherein the modifying substep includes estimating a blur kernel based on a focal length of the camera while obtaining the visual representation and based on the location of the face of the living being during the obtaining; calculating a focus position focal length based on the location of the face of the living being, the estimating of the blur kernel being based in part on the optimal focus position and/or the calculated focus position; and determining an identity of the living being based on the ascertained shape of the face of the living being and based on the captured image of the iris of the living being. 